1. Field of the Invention
Aspects of the present invention generally relate to a closed-loop multi-antenna system. More particularly, aspects of the present invention relate to an apparatus and method for transmitting/receiving data under a multi-user environment in a closed-loop multi-antenna system.
2. Description of the Related Art
Unlike wired channels, wireless channels have low transmission reliability due to multipath interference, shadowing, propagation attenuation, time-variant noise, interference, etc. Low transmission reliability is a main reason for failing to increase data rate in mobile communications.
To overcome the problem, many techniques have been proposed. Major examples of these techniques are error control coding and diversity. Error control coding mitigates the effects of signal distortion and noise. Diversity copes with fading by receiving a plurality of signals experiencing different fading.
Diversity schemes are categorized into time diversity, frequency diversity, multipath diversity, and spatial diversity. For time diversity, channel coding is combined with interleaving. For frequency diversity, signals transmitted at different frequencies are received in different multiple paths. Multipath diversity is a scheme that achieves a diversity effect by separating multipath signals using different fading information. A spatial diversity effect is achieved through independent fading signals using a plurality of antennas in a transmitter, a receiver, or both. An antenna array is generally used for spatial diversity.
A system using an antenna array (a multi-antenna system) has multiple antennas at a transmitter/receiver and exploits space to increase frequency efficiency. The spatial utilization facilitates an increase in data rate under the confines of time and frequency.
The multi-antenna system is also called a Multiple-Input Multiple-Output (MIMO) system in that the system transmits independent information through the respective antennas. The MIMO system requires an efficient signal processing algorithm to provide a high-quality, high-speed data service.
One such signal processing algorithm is a resource allocation algorithm. The resource allocation algorithm aims to allocate resources, such as data rates, to different antennas so as to achieve a target error rate with minimal resource consumption. The resource allocation algorithm can be considered as separate signal processing algorithms for a transmitter and a receiver. Given a data rate for each antenna, the transmitter seeks to allocate resources to the antennas with minimal energy consumption, aiming for the target error rate.
Conventionally, resource allocation is classified into uniform allocation, fixed allocation, and full-search allocation. The uniform allocation scheme allocates the same data rate to the antennas. This is the simplest resource allocation method without feedback. However, despite Successive Interference Cancellation (SIC), let alone linear detection, the uniform allocation scheme does not perform well because of a high error rate.
In the fixed allocation scheme, an optimal resource allocation is decided for one channel and is applied commonly to all channels. The optimal resource allocation is based on statistical analysis. Therefore, if the analysis holds true, the fixed allocation scheme performs better than the uniform allocation scheme. However, the constraint of fixed allocation limits error rate performance and makes the fixed allocation scheme ineffective against channel changes.
The full-search allocation scheme uses all possible combinations as data rate candidates and allocates a candidate requiring the least power to a current channel. The exhaustive search results in the best performance but increases complexity and the amount of feedback information. In this context, an iterative algorithm has been proposed for full-search allocation in order to decrease complexity.
The signal processing algorithm of the receiver is about assessing the status of each transmission channel and feeds the assessed channel information back to the transmitter, whereas the signal processing algorithm of the transmitter is about allocating resources to transmit antennas based on the feedback from the receiver.
A major signal processing algorithm for the transmitter is Bell Labs Layered Space Time (BLAST). BLAST increases the amount of transmitted data without expanding a frequency area used in the system by use of multiple antennas. Diagonal-BLAST (D-BLAST) and Vertical-BLAST (V-BLAST) algorithms are used. D-BLAST organizes data blocks along diagonals in space and time by using a particular block coding for data to be transmitted through respective transmit antennas. Despite the benefit of high frequency efficiency, D-BLAST suffers from implementation complexity. V-BLAST, on the other hand, reduces complexity by transmitting independent data through each antenna.
Signal processing algorithms for the receiver rely on linear detection or non-linear detection to detect signals transmitted from transmit antennas using a received signal. Linear detection techniques include Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). In the ZF technique, the receiver calculates the norms of the column vectors of a channel matrix, detects a symbol corresponding to a column vector with the largest norm, and eliminates the detected signal component from a received signal, thereby canceling interference between symbols. The MMSE technique minimizes the mean squared errors between original transmitted symbols and signals estimated by the receiver.
Non-linear detection techniques include Maximum Likelihood (ML) detection and SIC. The ML detection technique can improve performance significantly by selecting a signal having a minimum squared Euclidean distance to a received signal from among all possible symbols that can be transmitted from all transmit antennas. However, complexity increases exponentially with the number of transmit antennas and a modulation order. Although the ML detection technique performs best, it requires a large amount of computation, thus increasing implementation complexity.
SIC is an interference cancellation technique that detects a channel with a large Signal-to-interference plus Noise Ratio (SINR) first of all and eliminates the channel in order to improve performance. SIC additionally requires ordering to achieve the best performance.
As described above, there exists a need for a signal detection scheme and a resource allocation scheme that enables more accurate detection of a transmitted signal from a received signal and is advantageous in terms of computation volume in order to improve performance in a MIMO system.